Current prior art monitoring of bearings and wheel to rail interactions on train consists has been managed primarily through the use of wayside detectors located throughout the rail system, which includes detectors for monitoring the temperature of railcar wheel bearings, and wheel impact load detectors which identify damaged wheels by monitoring impacts of wheels on the rails. These detectors are installed at fixed points in the rail network.
Since their introduction, these methods have provided railroad operators with information to improve railcar and train consist performance. However, these detectors lack the benefits of a wireless network capable of transmitting information and data regarding operational anomalies, such as when a railcar derails, the condition of the bearings and wheels when not in range of detectors, and wheel damage. Further, these prior methods do not provide a mechanism to continuously monitor assets at any location in the rail network.
Wheel damage in the railroad industry is responsible for significant maintenance costs related to the railcar wheels, railcar body, railcar components, rail tracks and rail ties. Wheels that are slid flat have an uneven section on the wheel where it comes into contact with the rail. As the wheel rotates this section creates an abnormal impact pattern, which can cause further damage to the wheel, damage to the railcar and damage to the rail and track structure.
Presently, however, there is no reliable system for continuously monitoring the temperature of wheel bearings or wheel to rail interactions where a wheel impact load detector is not installed on a section of rail or in the area between detectors. Accordingly, it is desirable to provide a system and method for the real-time, on-board monitoring of various operational parameters of a railcar and/or train consists, and for analyzing the readings in real time to predict or timely detect anomalous operational conditions.